In recent years, we have focused on developing robust general image analysis methodology, culminating in our pattern recognition tool called WND-CHRM. We have validated this pattern-recognition approach to biological image analysis using diverse imaging modalities ranging from fluorescence microscopy to X-rays of human knees, MRIs, and other imaging modalities. We have also validated a range of applications from scoring image-based assays to diagnosis of disease to prediction of future disease risk. The specific applications of this approach are covered in reports AG000674-10 and AG000685-07. A major effort recently has been to rewrite the WND-CHARM code-base to make it more modular, better organized, easier to use, and accessible with the Python scripting language. A recent release of WND-CHARM 1.52 is available from our code repository (http://ome.grc.nia.nih.gov/wnd-charm/wndchrm-1.52.775.tar.gz). This release focuses on streamlining access to image feature computing algorithms so that they are individually acessible by the name of the algorithm and any preceding image transforms. The individual algorithms are also broken out behind a common interface so that new algorithms are easy to add. The image feature computing plan was previously hard-coded, but to make it more flexible, easier to modify, and responsive to on demand feature requests, it is now automatically assembled based on the requested features, taking account of their preceding image transform dependencies. Whole-image analysis has proven very useful, but it is not always possible to compare whole images to each other. Examples of relatively homogenous images are those of cultured cells, or tissues like muscle, liver, and certain types of tumors. Our work on human knee X-Rays (see AG000685-07) was the first application where a certain degree of pre-processing was necessary to align images of different subjects to compare them to each other. In this case, we simply found the center of the knee joint in each image and extracted a fixed radius around this center for all patients. A much more complicated alignment problem exists in images with complex anatomy. Possibly the most extreme example of this are stained sections of brain tissue. A solution to the alignment problem would allow the use of generalized pattern recognition to address morphological differences in an anatomical context. Spatially-resolved pattern analysis places an extreme burden on the performance of our software. Instead of an entire image being considered at once, or split into a small number of tiles on a grid, to achieve spatial resolution, each image must be sampled thousands or millions of times. In order to make this type of application practical, the computational strategy used in the software must be reconsidered. Previously, all 3,000 low-level image features were calculated for each image sample, even when most of them were later found to be irrelevant to the classification problem because they lacked discrimination power. The major change in strategy to enable spatially-resolved pattern recognition is to eliminate unnecessary calculations. This requires an on-demand computing strategy for image features, which is a major architectural goal for the wndchrm software. Our current release makes use of this strategy, exposing a very simple to use API for specifying the specific features to be computed. We have have also made the majority of the underlying C++ code accessible from the Python scripting language to make it easier to customize how WND-CHARM is used in new applications. It is now possible to compute on-demand features using the Python interface and perform further processing using mathematical and scientific computing libraries available for Python (numpy, scipy). A major feature expanded in the past year is the file of files interface. Previously this was simply a spreadsheet of individdual single-plane tiff files and the classes they belong to. Now, this interface allows the use of multi-dimensional (5D) image files to construct classification experiments, as well as allowing the combination of multiple image planes into extended multi-plane feature sets. An example of multi-plane feature sets is in the use of separate Hematoxylin and Eosin channels of the same image to compute a compound set of image descriptors. Other examples include combining multiple planes from a 3D experiment. These experiments previously required specialized code, but can now be accomplished simply by editing a file of files spreadsheet. The Python-related software is publicly available on our new public code repository (https://github.com/wnd-charm/wnd-charm). In 2012, in collaboration with Jason Swedlow (University of Dundee, Scotland), a large international project sponsored by the Wellcome Trust was initiated to develop specific applications of the OME/OMERO system. Our group's contribution to this project involves providing interfaces between OMERO and WND-CHARM to enable image comparisons in large, diverse image repositories. The eventual goal is to use pattern recognition to annotate new images added to these collections automatically, based on previously annotated images and a large set of independent classifiers that opeerate autonomously in the background. The primary design goal of a system like OME/OMERO is to provide scientists with easy ways of organizing and annotating their image collections. The organizational structure of the images and their grouping by their annotations can also serve as the primary inputs for training pattern-recognition classifiers. Because classifiers require little or no additional input from the user, the natural convergence of these two technologies represent a powerful new mode for maximizing the utility of large scale scientific and medical image databases. Currently we have a functioning prototype that interacts with OMERO to read image data and annotations; use these for training a classifier; and return annotations derived from classification back to OMERO. Substantial work remains to make this integrated system practical. Currently, a developer-preview release is available, but it lacks the flexibility on the OMERO side to keep track of multiple, potentially conflicting classifications for images, as well as maintaining a flexible store of image feature sets. Our current efforts are to implement a standardized feature store based on the widely used HDF data format, which can be used both standalone with WND-CHARM alone, and when using WND-CHARM together with the OME database.